Sign Language Recognition Information System Development Using Wireless Technologies for People with Hearing Impairments

2019 ◽  
Author(s):  
Artem Goncharenko ◽  
Lilia Voronova ◽  
Mikhail Artemov ◽  
Vjacheslav Voronov ◽  
Danil Bezumnov
Keyword(s):  
Information System ◽  
Sign Language ◽  
System Development ◽  
Language Recognition ◽  
Hearing Impairments ◽  
Sign Language Recognition ◽  
Information System Development ◽  
Wireless Technologies

Implementation of Booking Performance Application Using Bootstrap Case Study in PT XYZ

2019 ◽  
Vol 4 (1) ◽  
pp. 10
Author(s):  
Arfan Sansprayada ◽  
Kartika Mariskhana
Keyword(s):  
Information System ◽  
Business Processes ◽  
System Development ◽  
Basic Requirement ◽  
Application Development ◽  
Information System Development ◽  
Special Expertise ◽  
A Company ◽  
Need For Information

Abstract—The need for information system development in a company is a basic requirement that must be met by each company in order to run its business processes properly. This is the basic key in a company in order to provide maximum results to find as many profits or profits. Application development or requirements in the application also provide speed for employees to carry out their activities to work properly and optimally. The development of the era requires that companies must be productive and have innovations so that the business wheel of the company can run well. This is based on the development of technology that is so fast that it requires special expertise in its application. This research is expected to be able to help some problems that exist in a company. Where its application can make it easier for employees to carry out their respective duties and roles in order to maximize their potential. For companies, the application of this application can accommodate the company's business wheels so that they can be properly and correctly documented .   Keywords : Systems, Information, Applications

American Sign Language Recognition Based on MobileNetV2

2020 ◽  
Vol 5 (6) ◽  
pp. 481-488
Author(s):  
Kin Yun Lum ◽  
Yeh Huann Goh ◽  
Yi Bin Lee
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
American Sign ◽  
Language Recognition ◽  
Sign Language Recognition

INDIAN SIGN LANGUAGE RECOGNITION SYSTEM USING OPENPOSE

2019 ◽  
Vol 7 (2) ◽  
pp. 43
Author(s):  
MALHOTRA POOJA ◽  
K. MANIAR CHIRAG ◽  
V. SANKPAL NIKHIL ◽  
R. THAKKAR HARDIK ◽  
◽  
...  
Keyword(s):  
Sign Language ◽  
Recognition System ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Indian Sign Language

STATIC DEVNAGARI SIGN LANGUAGE RECOGNITION

2016 ◽  
Vol 3 (3) ◽  
pp. 13
Author(s):  
VERMA VERSHA ◽  
PATIL SANDEEP B. ◽  
◽  
Keyword(s):  
Sign Language ◽  
Language Recognition ◽  
Sign Language Recognition

Geographic information system development in the CARETS project

1977 ◽  
Author(s):  
William B. Mitchell ◽  
Robin G. Fegeas ◽  
Katherine A. Fitzpatrick ◽  
Cheryl A. Hallam
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
System Development ◽  
Geographic Information ◽  
Information System Development

Sign Language Recognition Techniques - A Survey

2020 ◽  
Vol 24 (5) ◽  
pp. 2747-2760
Author(s):  
Mahidar P.R.
Keyword(s):  
Sign Language ◽  
Language Recognition ◽  
Sign Language Recognition

Application of Google Earth COM API and KML in Tourism Information System Development

2011 ◽  
Vol 12 (6) ◽  
pp. 828-834 ◽  
Author(s):  
Liguang MA ◽  
Yanrong CAO
Keyword(s):  
Information System ◽  
System Development ◽  
Google Earth ◽  
Information System Development ◽  
Tourism Information

Technological Aids for Deaf and Mute in Modern World

2020 ◽  
Vol 14 ◽  
Author(s):  
Vasu Mehra ◽  
Dhiraj Pandey ◽  
Aayush Rastogi ◽  
Aditya Singh ◽  
Harsh Preet Singh
Keyword(s):  
Sign Language ◽  
Gesture Recognition ◽  
Recognition System ◽  
Modern World ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Background Elimination ◽  
Sign Recognition ◽  
Technological Advances ◽  
Better Than

Background:: People suffering from hearing and speaking disabilities have a few ways of communicating with other people. One of these is to communicate through the use of sign language. Objective:: Developing a system for sign language recognition becomes essential for deaf as well as a mute person. The recognition system acts as a translator between a disabled and an able person. This eliminates the hindrances in exchange of ideas. Most of the existing systems are very poorly designed with limited support for the needs of their day to day facilities. Methods:: The proposed system embedded with gesture recognition capability has been introduced here which extracts signs from a video sequence and displays them on screen. On the other hand, a speech to text as well as text to speech system is also introduced to further facilitate the grieved people. To get the best out of human computer relationship, the proposed solution consists of various cutting-edge technologies and Machine Learning based sign recognition models which have been trained by using Tensor Flow and Keras library. Result:: The proposed architecture works better than several gesture recognition techniques like background elimination and conversion to HSV because of sharply defined image provided to the model for classification. The results of testing indicate reliable recognition systems with high accuracy that includes most of the essential and necessary features for any deaf and dumb person in his/her day to day tasks. Conclusion:: It’s the need of current technological advances to develop reliable solutions which can be deployed to assist deaf and dumb people to adjust to normal life. Instead of focusing on a standalone technology, a plethora of them have been introduced in this proposed work. Proposed Sign Recognition System is based on feature extraction and classification. The trained model helps in identification of different gestures.

Indian Sign Language Recognition on PYNQ Board

2020 ◽  
Vol 13 ◽  
Author(s):  
Sukhendra Singh ◽  
G. N. Rathna ◽  
Vivek Singhal
Keyword(s):  
Sign Language ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Hand Gestures ◽  
Depth Images ◽  
Kinect Camera ◽  
Impaired People ◽  
Web Camera ◽  
Indian Sign Language

Introduction: Sign language is the only way to communicate for speech-impaired people. But this sign language is not known to normal people so this is the cause of barrier in communicating. This is the problem faced by speech impaired people. In this paper, we have presented our solution which captured hand gestures with Kinect camera and classified the hand gesture into its correct symbol. Method: We used Kinect camera not the ordinary web camera because the ordinary camera does not capture its 3d orientation or depth of an image from camera however Kinect camera can capture 3d image and this will make classification more accurate. Result: Kinect camera will produce a different image for hand gestures for ‘2’ and ‘V’ and similarly for ‘1’ and ‘I’ however, normal web camera will not be able to distinguish between these two. We used hand gesture for Indian sign language and our dataset had 46339, RGB images and 46339 depth images. 80% of the total images were used for training and the remaining 20% for testing. In total 36 hand gestures were considered to capture alphabets and alphabets from A-Z and 10 for numeric, 26 for digits from 0-9 were considered to capture alphabets and Keywords. Conclusion: Along with real-time implementation, we have also shown the comparison of the performance of the various machine learning models in which we have found out the accuracy of CNN on depth- images has given the most accurate performance than other models. All these resulted were obtained on PYNQ Z2 board.

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